Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910249233940480 |
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| author | Liu, Yuyu Yang, Jiannan Yu, Ziyang Pan, Weishen Wang, Fei Ma, Tengfei |
| author_facet | Liu, Yuyu Yang, Jiannan Yu, Ziyang Pan, Weishen Wang, Fei Ma, Tengfei |
| contents | Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputation approaches, which often assume uniformity and data completeness, struggle to address cases with large patches of missing data. In this paper, we present CROT (Cluster-Regularized Optimal Transport), an optimal transport-based imputation algorithm designed to handle patch-based missing data in tabular formats. Our approach effectively captures the underlying data structure in the presence of significant missingness. Notably, it achieves superior imputation accuracy while significantly reducing runtime, demonstrating its scalability and efficiency for large-scale datasets. This work introduces a robust solution for imputation in heterogeneous, high-dimensional datasets with structured data absence, addressing critical challenges in both biological and clinical data analysis. Our code is available on GitHub, https://github.com/yuyuliu11037/CROT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14653 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport Liu, Yuyu Yang, Jiannan Yu, Ziyang Pan, Weishen Wang, Fei Ma, Tengfei Machine Learning Genomics Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputation approaches, which often assume uniformity and data completeness, struggle to address cases with large patches of missing data. In this paper, we present CROT (Cluster-Regularized Optimal Transport), an optimal transport-based imputation algorithm designed to handle patch-based missing data in tabular formats. Our approach effectively captures the underlying data structure in the presence of significant missingness. Notably, it achieves superior imputation accuracy while significantly reducing runtime, demonstrating its scalability and efficiency for large-scale datasets. This work introduces a robust solution for imputation in heterogeneous, high-dimensional datasets with structured data absence, addressing critical challenges in both biological and clinical data analysis. Our code is available on GitHub, https://github.com/yuyuliu11037/CROT. |
| title | Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2601.14653 |